default_dag_args = {
'email_on_failure': False,
'email_on_retry': False,
'retries': 0,
# 'retry_delay': datetime.timedelta(minutes=5),
'start_date': datetime.datetime.today() - datetime.timedelta(days=1)
}
with models.DAG('lastfm-1k-ingest',
schedule_interval=datetime.timedelta(days=1),
default_args=default_dag_args) as dag:
dataflow = dataflow_operator.DataFlowPythonOperator(
task_id='ingest-users-dataflow',
py_file='gs://{}/lastfm-dataset-1K/code/ingest-users.py'.format(
PROJECT),
job_name='ingest-users-dataflow',
py_options=[],
dataflow_default_options={
'project': PROJECT,
'region': 'europe-west1'
},
options={},
poll_sleep=30)
start = bash_operator.BashOperator(task_id='start',
bash_command='echo "Start"')
end = bash_operator.BashOperator(task_id='end', bash_command='echo "End"')
start >> dataflow >> end
# Args required for the Dataflow job.
job_args = {
'input': INPUT_BUCKET_CSV,
# TODO: Populate the models.Variable.get() with the variable name for BQ table
'output': models.Variable.get('bq_output_table'),
# TODO: Populate the models.Variable.get() with the variable name for input field names
'fields': models.Variable.get('input_field_names'),
'load_dt': DS_TAG
}
# Main Dataflow task that will process and load the input delimited file.
# TODO: Specify the type of operator we need to call to invoke DataFlow
dataflow_task = dataflow_operator.DataFlowPythonOperator(
task_id="process-delimited-and-push",
py_file=DATAFLOW_FILE,
options=job_args)
# Here we create two conditional tasks, one of which will be executed
# based on whether the dataflow_task was a success or a failure.
success_move_task = python_operator.PythonOperator(
task_id='success-move-to-completion',
python_callable=move_to_completion_bucket,
# A success_tag is used to move
# the input file to a success
# prefixed folder.
op_args=[COMPLETION_BUCKET, SUCCESS_TAG],
provide_context=True,
trigger_rule=TriggerRule.ALL_SUCCESS)
failure_move_task = python_operator.PythonOperator(
default_args=DEFAULT_DAG_ARGS) as dag:
# Args required for the Dataflow job.
job_args = {
'input':
'gs://{{ dag_run.conf["bucket"] }}/{{ dag_run.conf["name"] }}',
'output': 'compose-test-291802:lake.SALES_DATA',
'runner': 'DataflowRunner',
'project': 'compose-test-291802',
'job_name': 'job-name-001',
'temp_location': 'gs://sanch-test-bucket12/tmp/',
'staging_location': 'gs://sanch-test-bucket12/stg',
'load_dt': DS_TAG
}
# Main Dataflow task that will process and load the input delimited file.
dataflow_task = dataflow_operator.DataFlowPythonOperator(
task_id="dataflowstoragebq1", py_file=DATAFLOW_FILE, options=job_args)
# Here we create two conditional tasks, one of which will be executed
# based on whether the dataflow_task was a success or a failure.
success_move_task = python_operator.PythonOperator(
task_id='success-move-to-completion',
python_callable=move_to_completion_bucket,
# A success_tag is used to move
# the input file to a success
# prefixed folder.
op_args=[COMPLETION_BUCKET, SUCCESS_TAG],
provide_context=True,
trigger_rule=TriggerRule.ALL_SUCCESS)
failure_move_task = python_operator.PythonOperator(
task_id='failure-move-to-completion',
job_args = {
'input': 'gs://{{ dag_run.conf["bucket"] }}/{{ dag_run.conf["name"]}}',
'output_raw':
'RAW_{{dag_run.conf["name"][:dag_run.conf["name"].rfind("/")]}}.{{"_".join(dag_run.conf["name"][:dag_run.conf["name"].rfind(".")].split("_")[2:])}}', # takes out the file name removing ".csv" from it
'output_err':
'PRZ_{{dag_run.conf["name"][:dag_run.conf["name"].rfind("/")]}}.{{"_".join(dag_run.conf["name"][:dag_run.conf["name"].rfind(".")].split("_")[2:])_ERR}}',
'output_prz':
'PRZ_{{dag_run.conf["name"][:dag_run.conf["name"].rfind("/")]}}.{{"_".join(dag_run.conf["name"][:dag_run.conf["name"].rfind(".")].split("_")[2:])}}',
'fields': g_fields,
'load_dt': '{{ dag_run.conf["bqTimestamp"]}}',
'op_dict': g_operations_dict
}
# Main Dataflow task
TSK_dataflow_file_ingestion = dataflow_operator.DataFlowPythonOperator(
task_id="tsk-dataflow-file-ingestion",
py_file=DATAFLOW_FILE,
options=job_args)
# Upon Dataflow task success the TSK_move_into_arc_bucket starts
TSK_move_into_arc_bucket = python_operator.PythonOperator(
task_id='TSK_move_into_arc_bucket',
python_callable=DPLF_move_into_arc_bucket,
op_args=[g_output_bucket],
provide_context=True,
trigger_rule=TriggerRule.ALL_SUCCESS)
# Upon Dataflow task failure the TSK_move_into_inv_bucket starts
TSK_move_into_inv_bucket = python_operator.PythonOperator(
task_id='TSK_move_into_inv_bucket',
python_callable=DPLF_move_into_inv_bucket,
op_args=[g_failed_bucket],
def create_dag(env_variables):
"""Creates the Airflow directed acyclic graph.
Args:
env_variables: Dictionary of Airflow environment variables.
Returns:
driblet_dag: An instance of models.DAG.
"""
driblet_dag = initialize_dag()
# Clients setup.
project_id = env_variables['project_id']
bq_client = bigquery.Client(project=project_id)
gcs_client = storage.Client(project=project_id)
# TASK 1: Convert BigQuery CSV to TFRECORD.
dag_dir = configuration.get('core', 'dags_folder')
transformer_py = os.path.join(dag_dir, 'tasks/preprocess',
'transformer.py')
bq_to_tfrecord = dataflow_operator.DataFlowPythonOperator(
task_id='bq-to-tfrecord',
py_file=transformer_py,
options={
'project':
project_id,
'predict-data':
'{}.{}.{}_{}'.format(project_id, env_variables['bq_dataset'],
env_variables['bq_input_table'],
datetime.datetime.now().strftime('%Y%m%d')),
'data-source':
'bigquery',
'transform-dir':
'gs://%s/transformer' % env_variables['bucket_name'],
'output-dir':
'gs://%s/input' % env_variables['bucket_name'],
'mode':
'predict'
},
dataflow_default_options={'project': project_id},
dag=driblet_dag)
# TASK 2: Make prediction from CSV in GCS.
make_predictions = mlengine_operator.MLEngineBatchPredictionOperator(
task_id='make-predictions',
project_id=project_id,
job_id='driblet_run_{}'.format(
datetime.datetime.now().strftime('%Y_%m_%d_%H_%M_%S')),
data_format='TF_RECORD',
input_paths=['gs://%s/input/predict-*' % env_variables['bucket_name']],
output_path='gs://%s/output' % env_variables['bucket_name'],
region=env_variables['region'],
model_name=env_variables['model_name'],
version_name=env_variables['model_version'],
gcp_conn_id='google_cloud_default',
dag=driblet_dag)
# TASK 3: Export predicted CSV from Cloud Storage to BigQuery.
job_config = bigquery.LoadJobConfig()
job_config.autodetect = True
job_config.source_format = bigquery.SourceFormat.NEWLINE_DELIMITED_JSON
job_config.time_partitioning = bigquery.TimePartitioning(
type_=bigquery.TimePartitioningType.
DAY, # Sets daily partitioned table.
expiration_ms=env_variables['dataset_expiration'])
gcs_to_bigquery = GCStoBQOperator(
task_id='gcs-to-bigquery',
bq_client=bq_client,
gcs_client=gcs_client,
job_config=job_config,
dataset_id=env_variables['bq_dataset'],
table_id=env_variables['bq_output_table'],
gcs_bucket=env_variables['bucket_name'],
gcs_location=env_variables['location'],
exclude_prefix='errors_stats', # Exclude files starting with name.
dir_prefix='output',
dag=driblet_dag)
# TASK 4: Delete files in Cloud Storage bucket.
gcs_delete_blob = GCSDeleteBlobOperator(
task_id='gcs-delete-blob',
client=gcs_client,
gcs_bucket=env_variables['bucket_name'],
prefixes=['input', 'output'],
dag=driblet_dag)
make_predictions.set_upstream(bq_to_tfrecord)
make_predictions.set_downstream(gcs_to_bigquery)
gcs_delete_blob.set_upstream(gcs_to_bigquery)
return driblet_dag
# Setting schedule_interval to None as this DAG is externally trigger by a Cloud Function.
with models.DAG(dag_id='mssql_gcs_dataflow_bigquery_dag_2',
description='A DAG triggered by an external Cloud Function',
schedule_interval=None,
default_args=DEFAULT_DAG_ARGS) as dag:
# Args required for the Dataflow job.
job_args = {
'input':
'gs://{{ dag_run.conf["bucket"] }}/{{ dag_run.conf["name"] }}',
'output': models.Variable.get('bq_output_table'),
'load_dt': DS_TAG
}
# Main Dataflow task that will process and load the input delimited file.
dataflow_task = dataflow_operator.DataFlowPythonOperator(
task_id="process-json-to-dataflow",
py_file=DATAFLOW_FILE,
options=job_args)
# Here we create two conditional tasks, one of which will be executed
# based on whether the export_sales_orders was a success or a failure.
success_move_task = email_operator.EmailOperator(
task_id='success',
trigger_rule=TriggerRule.ALL_SUCCESS,
to=models.Variable.get('email'),
subject=
'mssql_gcs_dataflow_bigquery_dag_2 Job Succeeded: start_date {{ ds }}',
html_content="HTML CONTENT")
failure_move_task = email_operator.EmailOperator(
task_id='failure',
trigger_rule=TriggerRule.ALL_FAILED,
return df.values[-1][0], df.values[-1][1]
def tell_slack(context):
o = slack_webhook_operator.SlackWebhookOperator(task_id="tell_slack", http_conn_id='slack_default',
message="Number one page today is %s (%s hits)" % (
find_number_one()))
return o.execute(context)
with models.DAG(
'ga_daily_reporter',
schedule_interval=datetime.timedelta(days=1),
default_args=default_dag_args) as dag:
benchmark_tally = dataflow_operator.DataFlowPythonOperator(task_id='benchmark_tally',
py_file='/home/airflow/gcs/dags/pipelines/benchmark_tally.py')
combine_tally = python_operator.PythonOperator(
task_id='combine_tally',
python_callable=combine_tally,
on_success_callback=tell_slack)
# on_success_callback is a hack to delay generating the slack message
# https://stackoverflow.com/questions/52054427/how-to-integrate-apache-airflow-with-slack
tell_slack = slack_webhook_operator.SlackWebhookOperator(task_id="tell_slack", http_conn_id='slack_default',
message="A new report is out: "
"https://%s/data/tally_69211100_20190425.csv" % (
models.Variable.get('AIRFLOW_BUCKET',
'us-east1-dta-airflow-b3415db4-bucket')))
generate_graph = python_operator.PythonOperator(
task_id='generate_graph',
python_callable=generate_graph)
'email_on_retry': False
}
dag = DAG(
'twitter_search',
default_args=default_args,
description='Load data from GCS to BQ Serving Layer',
schedule_interval='@daily',
dagrun_timeout=timedelta(minutes=30)
)
load_raw_data = dataflow_operator.DataFlowPythonOperator(
task_id='load_raw_data',
dag=dag,
py_file='/home/airflow/gcs/dags/dataflow/twitter-google-dataflow.py',
#py_file='dataflow/twitter-google-dataflow.py',
job_name='twitter-google-dataflow-{{ ds }}',
dataflow_default_options={'project':os.environ.get('GCP_PROJECT'), 'region': 'europe-west1','zone':'europe-west6-a','runner':'DataflowRunner'},
options={'job_date':'{{ ds }}', 'twitter_bucket':os.environ.get('TWITTER_BUCKET'), 'dataflow_bucket':os.environ.get('DATAFLOW_BUCKET')}
)
#delete_sl_partition = bigquery_operator.BigQueryOperator( # TODO change to bq command line
# task_id='delete_sl_partition',
# dag=dag,
# sql='''DELETE FROM dataops_demo_sl_dev.t_twitter_google WHERE c_created = '{{ ds }}' ''',
# use_legacy_sql=False
#)
delete_sl_partition = bash_operator.BashOperator(
task_id='delete_sl_partition',
dag=dag,
job_args = {
'input':
'gs://{{ dag_run.conf["bucket"] }}/{{ dag_run.conf["name"] }}',
'output':
'seu-projeto-nome-no-google:dataNavigationDataSet.RAW_DATA_NAVIGATION',
'runner': 'DataflowRunner',
'project': 'seu-projeto-nome-no-google',
'job_name': 'job-name-001',
'temp_location':
'gs://seu-projeto-nome-no-google-bucket-navigation/tmp/',
'load_dt': DS_TAG
}
# Main Dataflow task that will process and load the input delimited file.
dataflow_task = dataflow_operator.DataFlowPythonOperator(
task_id="dataflow-b2w-raw-nav-data-001",
py_file=DATAFLOW_FILE,
options=job_args)
# Here we create two conditional tasks, one of which will be executed
# based on whether the dataflow_task was a success or a failure.
success_move_task = python_operator.PythonOperator(
task_id='success-move-to-completion',
python_callable=move_to_completion_bucket,
# A success_tag is used to move
# the input file to a success
# prefixed folder.
op_args=[COMPLETION_BUCKET, SUCCESS_TAG],
provide_context=True,
trigger_rule=TriggerRule.ALL_SUCCESS)
failure_move_task = python_operator.PythonOperator(
